class_docstr: Experience for Continual Reinforcement Learning purposes.
  
  The experience provides access to a `gym.Env` environment.
  Such environment can also be created lazily by
  providing a function.
code: def __init__(
      self,
      current_experience: int,
      origin_stream: CLStream,
      env: Union[Env, Callable[[], Env]],
      n_envs: int = 1,
      task_label: Optional[int] = None,
  ):
      super().__init__(current_experience, origin_stream)
      self.env = env
      self.n_envs = n_envs
      # task label to be (optionally) used for training purposes
      self.task_label = task_label
  
      self._as_attributes(
          "task_label", use_in_train=True, use_in_eval=True
      )
  
file_path: avalanche/avalanche/benchmarks/scenarios/reinforcement_learning.py
imports: from avalanche.benchmarks.scenarios import CLExperience, CLScenario, EagerCLStream
  from typing import Any, Callable, List, Optional, TypeVar, Union, Dict
  import numpy
  from avalanche.benchmarks.scenarios.generic_scenario import CLStream
label_desc: Use image node_id 1 to create a new RLExperience object from inherited base classes: CLExperience with example: obj = RLExperience(current_experience, origin_stream, env, n_envs, task_label)